Foundation models have rapidly permeated society, catalyzing a wave of generative AI
applications spanning enterprise and consumer-facing contexts. While the societal impact of …

Recent decisions by leading AI labs to either open-source their models or to restrict access
to their models has sparked debate about whether, and how, increasingly capable AI …

Large-scale pre-training has recently emerged as a technique for creating capable, general-
purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many …

Many generative foundation models (or GFMs) are trained on publicly available data and
use public infrastructure, but 1) may degrade the" digital commons" that they depend on, and …

As increasingly powerful generative AI systems are developed, the release method greatly
varies. We propose a framework to assess six levels of access to generative AI systems: fully …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

AI models and services are used in a growing number of high-stakes areas, resulting in a
need for increased transparency. Consistent with this, several proposals for higher quality …

Transparency is now a fundamental principle for data processing under the General Data
Protection Regulation. We explore what this requirement entails for artificial intelligence and …

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are
rapidly transforming the way we communicate, illustrate, and create. However, AI regulation …

There have been increasing calls for centering impacted communities–both online and
offline–in the design of the AI systems that will be deployed in their communities. However …